The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Agricultural production requires significant strategy and analysis. In many cases, agricultural growers, such as farmers or others involved in agricultural cultivation, are required to analyze a variety of data to make strategic decisions months in advance of the period of crop cultivation known as the growing season. In making such strategic decisions, growers may consider fuel and resource costs, historical and projected weather trends, soil conditions, projected risks posed by pests, disease and weather events, and projected market values of agricultural commodities or crops. Analyzing these decision constraints may help a grower to predict key agricultural outcomes including crop yield, energy usage, cost and resource utilization, and farm profitability. Such analysis may inform a grower's strategic decisions of determining crop cultivation types, methods, and timing.
Despite its importance, such analysis and strategy is difficult to accomplish for a variety of reasons. First, obtaining reliable information for the various considerations of the grower is often difficult. Second, aggregating such information into a usable manner is a time consuming task. Third, where data is available, it may not be precise enough to be useful to determine strategy. For example, historical or projected weather data is often generalized for a large region such as a county or a state. In reality, weather may vary significantly at a much more granular level, such as an individual field. In addition, terrain features may cause weather data to vary significantly in even small regions.
Additionally, current climatology systems lack information that may be useful to farmers in making decisions regarding the fields. For example, most climatology systems use point estimates without factoring in possible errors, deviations, or risk of anomalous events. These errors may propagate when fed into an agronomic model, such as models that estimate heat stress or growing degree days for crops. Accordingly, methods for accurately estimating temperature variables at a granular level are desirable.